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Ecological Economics

journal homepage: www.elsevier.com/locate/ecolecon

Methodological and Ideological Options

An Ecosystem Service Value Chain Analysis Framework: A Conceptual Paper

Jonathan M. Rawlinsa,⁎, Willem J. De Langeb, Gavin C.G. Frasera

a Department of Economics and Economic History, Rhodes University, Grahamstown 6140, South Africab Council for Scientific and Industrial Research (CSIR), Stellenbosch 7600, South Africa

A R T I C L E I N F O

Keywords:Ecosystem servicesValue chain analysisCausal loop diagram

A B S T R A C T

Modern day societies and economies are becoming increasingly vulnerable to the continued erosion of the stocksand flows of essential ecosystem services. Thus, the management of complex socio-economic systems to effec-tively provide these essential services has become a global priority policy and academic research area.Understanding how underlying processes and functions contribute towards the provision of final ecosystemservices can facilitate improved dissemination of credible, legitimate and salient information to decision-makers.This paper presents an ecosystem service value chain analysis framework that applies basic system dynamicsmodelling in the form of causal loop diagrams to facilitate an alternative analysis of ecosystem service valuechains. A scoping application of the framework is applied to a case study for flood attenuation services in theBaviaanskloof catchment in South Africa. The framework enables the identification of forward linkages andripple effects in individual value chains of final ecosystem services as well as the identification and assessment ofchallenges and opportunities within individual causal pathways. Ultimately, providing the potential to advancestrategies for improving the efficiency and effectiveness of final ecosystem service provision.

1. Introduction

Modern day societies and economies are becoming increasinglyvulnerable to the continued erosion of the stocks and flows of essentialecosystem services (ESs) (De Groot et al., 2010a; Vihervaara et al.,2010). The inherently conflicting nature of the current mainstreameconomic model and ecology provide complex, transdisciplinary andmulti-scalar management challenges faced with ever increasing eco-nomic costs of inaction (Stern, 2007; TEEB, 2010). Since the 1970s, thedevelopment of the ES concept has brought about a myriad of ESmanagement approaches (Gómez-Baggethun et al., 2010), yet tradi-tional neoclassical market structures and processes continue to under-provide ESs (Boyd and Banzhaf, 2007; Hanley et al., 2007) due to theirlack of integration into formal markets, the limitations of ES valuationand perverse incentive structures around the provision of certain ESs.This provides the incentive to develop transdisciplinary tools focusedon bridging the gap between ES valuation and practical, sustainable ESmanagement.

Global recognition of the economy as a sub-system of the environ-ment and rigorous scientific research in ESs is still developing, thus,robust means for modelling, mapping, valuing and measuring ESs areyet to be standardised (Gómez-Baggethun et al., 2016; Rockström et al.,2009; Sagoff, 2016; Small et al., 2017). New schools of thought pur-porting the interconnectedness and co-dependencies of environmental

sustainability and social justice (Raworth, 2017) further emphasise theimportance of holistic socio-ecological frameworks for ES managementto be able to reconcile the incongruity between ecology and economics(Gómez-Baggethun and Barton, 2012).

John Stuart Mill (1882), in his famous book A System of Logic, firstposited the notion of inductive inquiry, which structures any analysis ofthings in the natural science domain according to their individualcomponents. Understanding intricate socio-ecological systems requiresa clear definition of key system components and their associated causeand effect relationships as well as a description of the relationship of thesystem to other systems (De Groot et al., 2010b; Ford, 1999; Limburget al., 2002). Complexity is a characteristic common to all coupledhuman-environment systems (Loehe, 2004) and thus the challenge tocommunicate the functionality of such systems lies within explainingthe relationships between key elements of the system in a simple andtransparent way.

This paper presents and critically analyses an ecosystem servicevalue chain analysis (ESVCA) framework that applies basic system dy-namics modelling in the form of causal loop diagrams (CLDs) to facil-itate an alternative analysis of ES value chains that contribute towardsthe production of final ESs. The notion of value chain analysis and theconcept of CLDs as a modelling tool are discussed in relation to EStheory and management. We present a detailed step-by-step develop-ment of the ESVCA method and outline a scoping application in South

https://doi.org/10.1016/j.ecolecon.2017.12.023Received 17 April 2017; Received in revised form 3 November 2017; Accepted 14 December 2017

⁎ Corresponding author at: OneWorld Sustainable Investments, 3rd Floor Equity House, Cnr St George's Mall & Church Street, Cape Town 8001, South Africa.E-mail address: jrawlins@outlook.com (J.M. Rawlins).

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Africa. Lastly, the strengths and limitations of the approach are dis-cussed alongside directions for future research.

2. Value Chain Analyses and Ecosystem Services

Value chain analyses are conceptual frameworks used to map andcategorise chosen economic, social and environmental processes inservice and product value chains, ultimately aiming to help create abetter understanding of how and where enterprises and institutions arepositioned within the value chain and identifying opportunities andpotential leverage points for improvement (Sterman, 2000). Tradi-tionally, value chain analyses trace the value being added in each stepin the life cycle of a particular good or service, from the process ofproduction/harvesting through various steps of value adding until finalconsumption or utilisation and waste disposal (Baleta and Pegram,2014; Kaplinsky and Morris, 2000).

Since the popularisation of the ES concept in the MillenniumEcosystem Assessment (MA, 2005), defining and measuring ESs hasbeen the subject of significant scholarship (Potschin and Haines-Young,2016). The complex and dynamic nature of socio-ecological systemsmake system behaviour as a function of human and natural dis-turbances difficult to predict (Costanza, 2015; EC, 2013), thus the in-corporation of ES thinking into value chain assessments is still in itsinfancy. Complex system dynamics make provisioning and some reg-ulating services more amenable to a detailed analysis because of therelative ease of logic in determining multiple intermediate services (i.e.services that only provide benefits to humans indirectly through im-pacts on final ESs first) (Fisher et al., 2009; Johnston and Russell,2011).

Directly incorporating ESs into traditional market value chainanalyses, albeit uncommon, has been conducted for several provi-sioning services such as coral reef fish (Thyresson et al., 2013), timber(Van Den Berg et al., 2013) and shade-grown coffee (Jha et al., 2011).However, there has been no attempt to map and analyse the value chainof intermediate ESs that contribute towards the production of final ESs.

ESs have been indirectly addressed through approaches to increasethe sustainability of value chains, these include certification schemes,corporate social responsibility, risk management and mitigation in-itiatives (Grigg et al., 2009; Weiss et al., 2011). There have been nu-merous multi-actor activities addressing how biodiversity is and can beintegrated into value chains (Bolwig et al., 2010; Van Den Berg et al.,2013). Some of these include the IUCN Global Business and BiodiversityProgram (BBP) (Bishop et al., 2008), the EU Business and Biodiversityplatform, the UNDP protecting biodiversity in working with agribusi-ness project (Leibel, 2012) and the Business and Biodiversity OffsetsProgram (BBOP) (Van Den Berg et al., 2013). These initiatives and re-search endeavours emphasise the limits of market-based approaches forvalue chains, which range from unorganised and powerless workers andthe lack of true market values for ESs to difficulties in product andservice commercialisation (Wood, 2001).

Product and service value chains are geared towards linear pro-cesses and private goods that form part of a conventional neoclassicalmarket setup. Hence, the notion of incorporating public goods (such asESs) that generally do not have defined market values nor are traded informal markets, into a value chain analysis will require an alternativeapproach to conventional linear techniques (Henderson et al., 2002).

3. Causal Loop Diagrams

System dynamics is a branch of systems thinking theory often usedto explain intricate ecosystem structure and function and illustrate theoutcomes of potential management strategies by graphically re-presenting system feedback structures (Kirkwood, 2013; Richardsonand Pugh, 1989). CLDs, influence diagrams or cognitive maps are aqualitative diagramming language aimed at graphically illustratingfeedback-driven systems (Schaffernicht, 2010; Sterman, 2000). The

next stage involves defining stocks and flows of the system and quan-tifying the interactions between elements to incorporate associatedtime delays (Ford, 1999).

A typical CLD comprises of a group of symbols representing a par-ticular dynamic system's causal structure. This includes all relevantvariables, causal links with a polarity (either negative or positive) andsymbols which identify feedback loops and their polarity (Fernald et al.,2012). Each arrow is labelled with either a + or a − sign (polarity)which represents the cause-and-effect relationship between the twovariables. A + sign is used to represent a relationship where the twovariables change in the same direction while a − sign indicates that thevariables change in opposite directions (Kirkwood, 2013; Sterman,2000).

Conceptualising a complex ecological system not only requires aclear definition of the key elements of the system and the cause andeffect relationships between these elements, but also an account of therelationship of the system with other systems. The challenge to com-municate the functionality of such a system lies within explaining therelationships between key elements of the system in a simple andtransparent way. CLDs facilitate a common understanding and im-proved insight among stakeholders vis-à-vis how the system works andwhy it responds to external stimuli the way it does (Evans, 2004;Richardson, 1997). Such insight is immensely powerful for developingand strategising ecosystem management practices because it makescritical elements, which are within the stakeholder's control, explicit.

On the other hand, CLDs are limited in that they often fail to ac-curately distinguish between stock and flow variables, conserved flowsand information links, only illustrating basic relationships excludesother minor, potentially important, relationships (Hürlimann, 2009;Lane, 2008). CLDs are unable to signify current levels of one variable'sinfluence on another's behaviour or specifically how each influencefunctions (Schaffernicht, 2010). Consequently, the relationship be-tween event and behaviour is regularly counterintuitive. These limita-tions highlight how system behaviour is only inferred from CLDs, em-phasising the importance of verification and validation of systemprocesses.

Reynaud and Lanzanova (2017) quantitatively explored the antag-onistic and synergistic relationships between ESs provided by lakes, thisresearch highlighted the importance of understanding ES feedbacks andinteractions at the system scale. Advancing CLDs to represent naturalsystems addresses the challenge of building support and consensus formanagement strategies focusing on the trade-offs between environ-mental conservation and socio-economic benefits (Evans, 2004). Thesechallenges are derived mainly from difficulty in communicating thevalue of ESs and the complexity of the underlying ecosystems to a broadaudience consisting of different technical backgrounds and potentiallyconflicting perspectives (Costanza and Ruth, 1998; Lane, 2008;Morecroft, 1982).

4. Methods

4.1. Ecosystem Services Value Chain Analysis (ESVCA) Framework

Our ESVCA framework comprises the five iterative process stepsillustrated in Fig. 1. The method is separated into two distinct phases,the ‘CLD development’ phase (1–3) and the ‘CLD analysis’ phase (4–5).Further iterations of the analysis phase are possible once the problemand/or objective has been reconceptualised in light of a prior analysis.

Table 1 illustrates how each step of the above-mentioned processesis included in the two-phased approach. This two-phased approach isbased on an adaption and amalgamation of the major system dynamicsanalysis processes (A1–6) suggested by Ford (1999), the general three-step modelling process (B1–3) outlined by Costanza and Ruth (1998)and the practical value chain analysis steps (C1–3) put forward byMindtools (2014).

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4.2. Scoping Application

A qualitative scoping application of the ESVCA framework appliedto a case study for flood attenuation in the Baviaanskloof catchment,South Africa, is presented here. The aim of the application is to in-vestigate the efficacy of the framework and highlight strengths andweaknesses of the approach for future use.

4.2.1. Study AreaThe Baviaanskloof catchment is situated in Eastern Cape Province of

South Africa (Fig. 2). Several different user groups such as irrigatedagriculture, livestock and game farming, conservation and recreation/tourism compete for ESs at different scales (Illgner and Haigh, 2003;Nel et al., 2006). The catchment is characterised by a highly variableclimate and hydrological regime. The catchment is situated in a bi-modal rainfall zone with spring and autumn maxima, with an annualaverage of approximately 350 mm and a large interannual variability(ranging from less than 100 mm to greater than 700 mm) (Manderet al., 2010). The mean annual precipitation is characteristically 20% ofthe mean annual evaporation, resulting in arid conditions through mostof the catchment (Van Luijk et al., 2013). Floods pose a significantthreat to economic and social activities in the catchment as well asdownstream areas (Van Der Burg, 2008). Climate change and land-usemodels suggest extreme events such as flooding will occur more fre-quently in the future (Jansen, 2008).

Fire is a common occurrence in the Baviaanskloof and plays animportant role in veld management in the area (Boshoff, 2005, 2008).The area is governed by a natural fire regime, which is an essential partof ecological cycles and assists with the propagation of many endemicFynbos1 species (Booysen and Tainton, 1984). The use of restoration asa management tool to improve flood attenuation capacity, ecosystemhealth and water provision has grown rapidly over the last decade(Palmer et al., 2014). Catchment restoration includes the building ofweirs, gabions and small dams as well as the restoration of alluvial fansand the planting of Portulacaria afra2 in degraded areas (Illgner andHaigh, 2003; Powell, 2009). The ultimate purpose of these activities isto promote diffuse flow of water throughout the catchment in order toretain as much water as possible.

The Baviaanskloof River is a perennial river with a number of non-perennial tributaries that flow down the valley into the river, many of

which are canalised to prevent flooding on cultivated land (Jansen,2008). The Baviaanskloof makes up part of the Kougaberg StrategicWater Source Area, these are areas that supply a disproportionateamount of mean annual runoff to a geographical region of interest suchas large cities and biodiversity conservation areas (Nel et al., 2013).

4.2.2. ConceptualisationThe first process step involved conceptualising and delimiting the

nature of the problem and management challenge to be addressed (stepA1). Flooding was identified as a significant threat to the livelihoods ofpeople living in the basin and is directly affected by natural and an-thropogenic processes. Multiple stakeholders including insurance pro-viders, conservationists, researchers and farmers expressed interest ingraphically modelling the contribution of underlying processes to thesystem's ability to attenuate flooding. Stakeholders were identified incollaboration with two prominent research and advocacy organisationsworking in the area, then subsequently through recommendations.Delimiting the scope of the problem involved defining the physicalextent of the study area, relevant stakeholders and the particular finalES of interest (i.e. flood attenuation).

4.2.3. Expert WorkshopsThe second step encompassed hosting two expert workshops with

participants from academic and professional backgrounds in aquaticscience, geomorphology, environmental modelling, ecological eco-nomics and hydrology. The specific objectives of the workshop werethreefold:

i. Identify and describe final ESs that occur in the study areaii. Identify and describe associated intermediate ESsiii. Develop CLD

The problem definition relating to the objective of the study waspresented in the beginning of the workshop. The primary aim is to builddialog and facilitate a participatory approach towards developing CLDswith emphasis being placed on an agreed understanding of the inter-actions between the different final ESs and associated intermediateservices (Jafari et al., 2008; Koca and Sverdrup, 2012). Collaborativeengagement of experts has been proven to successfully facilitate thedetermination of the feasibility of different environmental risk man-agement scenarios (Ginsburg et al., 2010).

Addressing the first workshop objective involved participants con-ducting ‘blind’ identification of no less than three final ESs. The rig-orous, four-rule methodology for distinguishing between final and in-termediate ESs developed by Johnston and Russell (2011) was thenpresented and adopted to ensure a sound understanding of the differ-entiation between the two concepts. This understanding builds on thelogic that interlinked biophysical processes and structures are con-nected to human well-being by a sequence of intermediate functionsand processes, as illustrated by Potschin and Haines-Young (2011) asthe ‘ES cascade’.

Cooperative identification of final ESs in the study area was com-pleted in line with the Common International Classification ofEcosystem Services (CICES; EEA, 2017). The workshop followed a de-mand side (‘reverse engineering’) approach towards the development ofthe CLD, beginning with the final ESs and working backwards throughthe various intermediate services towards the processes and functionsthat contribute towards the supply of the final ESs. Participants sys-tematically identified a particular beneficiary group, then specificbenefits that contribute to the welfare of the beneficiary group and,lastly, the final ES that provides this benefit (e.g. flood attenuation).Flood attenuation, a regulating ESs, was identified as the primary finalES, however, water provision (provisioning) and aquatic ecosystemhealth (supporting) were included as final ESs to ensure adequatecoverage of related processes and functions. Building on Landers andNahlik (2013) and Landuyt et al. (2014) the selection of the final ESs for

2. Expert Workshop/s

3. Professional and Site Verification

4. Scenario Analysis

5. Value Chain Analysis

1. Conceptualisation

Fig. 1. ESVCA framework process cycle.

1 A shrubland biome and ecoregion endemic to South Africa.2 Small-leaved succulent endemic to South Africa.

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the CLD was based on the relevance of the services to the problemstatement, their level of integration into formal markets, whether thequality and/or quantity of delivery can be altered by potential man-agement scenarios and related information/knowledge availability.

The second objective was to identify, categorise and describe theassociated intermediate processes, variables, conditions or ESs that di-rectly or indirectly affect the previously identified final ESs. Aminimum of one intermediate ES was identified for each of the final ESsidentified during the first workshop objective. These were deemed firsttier intermediate services as they directly affect the final ESs. Onceconsensus was established around these intermediate services thenadditional tiers of intermediate services were identified. All inter-mediate ESs were distinguished into either environmental or anthro-pogenic CLD variables. An environmental variable is any process orvariable that occurs without or is independent of any direct humaninfluence (e.g. rainfall) and an anthropogenic variable is a process orvariable that occurs as a direct result of or is dependent on humanagency (e.g. water abstraction). Identifying and describing the neces-sary intermediate and final ESs completes steps A2 and C1.

System dynamics modelling software (Vensim PLE version 6.3) wasused to graphically generate the diagram and display all of the linkagesin real time during the workshop. The previously identified final andintermediate ESs were added to the CLD. Then the complex causal re-lationships and linkages between these ESs were systematically

identified and defined, while continuously making note of any factorsthat may affect the quantity, quality, timing and location of the affectedservice as per Brauman et al. (2007). Therefore, addressing steps A3and B1.

The workshops focused on producing adequately detailed CLDs thatminimise uncertainty yet maintain sufficient levels of complexity, the‘point of minimum uncertainty’ (Loucks et al., 2005). This involvedqualitative consensus building through trial-and-error of numerousenvironmental and anthropogenic processes and associated interac-tions, which were collaboratively ranked. No final cultural serviceswere included because of the high level of uncertainty around systemprocesses and interactions. Ultimately, this limited the output to onlythe most relevant and impactful variables affecting the final ESs so itcan be easily understood and communicated.

4.2.4. Professional and Site VerificationIn completing steps A4 and B2, formal meetings and interviews

were set up with professionals and relevant specialists in the study areato scrutinise the CLD. An open dialog was propagated around the rea-lism and accuracy of the diagram to facilitate the relevant knowledgeinput into the diagram in terms of specifically defining each variable,relationships between services and units of measurement. Consideringthe absence of mathematically defined relationships between the in-terconnected variables, the general flow logic of the CLD was tested and

Fig. 2. Baviaanskloof catchment, South Africa.

Table 1Process steps comprised in the ESVCA framework.

ESVCA framework process steps A: Major system dynamics analysis steps(Ford, 1999)

B: Three-step modelling process(Costanza and Ruth, 1998)

C: Value chain analysis steps(Mindtools, 2014)

1. Conceptualisation 1. Problem definition and delimiting2. Expert workshop 2. Describing the underlying system 1. Low resolution, high generality scoping model 1. Activity analysis

3. Model development3. Professional and site verification 4. Model verification 2. Improve on level of detail & create site-specific scenarios4. Scenario analysis 5. Modelling for analysis 3. Analyse scenario and management options 2. Value analysis

3. Evaluation and planning5. Value chain analysis

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Fig.

3.Aqu

atic

ecosystem

services

CLD

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the individual variables and components examined. Relevant dis-turbances, shocks or changes to the system (i.e. scenarios) were dis-cussed and investigated to verify the robustness of the diagram andidentify relevant scenarios for analysis to make it as suitable for thepurpose of the research as possible (Grösser, 2012).

4.2.5. Scenario AnalysesScenario analyses are a common approach for analysing trade-offs

between ES delivery and their implications for human well-being, mostnotably in the Millennium Ecosystem Assessment (MA, 2005). Con-cordantly, they are often an integral component of ES frameworks formanagement and decision-making (Guswa et al., 2014; Pirard et al.,2010). A particular system change or disturbance is identified and thenthe resultant impacts throughout the system are methodically analysedto further scrutinise the accuracy of the model and address the problemstatement. Each scenario either simulates a potential opportunity orchallenge that directly or indirectly affects the provision of a particularfinal ES.

Firstly, an accurate and detailed description of each scenario wasprovided. Each analysis was conducted using a supply side approach,beginning with the disturbance to the system then logically followingthe impact of the disturbance through the various linkages until thenature of the impact on the final ES could be determined. The outcomeof the analyses are CLDs illustrating the effect of the disturbance on thesystem through highlighted causal linkages (arrows), only the directand indirect impacts of the particular scenario are included.

The immediate, short and long-term impacts were distinguished bydifferent colour arrows in an attempt to compensate for CLD's limitedability to illustrate differences in temporal scales. Specifically, ‘short-term’ refers to impacts that occur within days or weeks of the dis-turbance while ‘medium/long-term’ considers months to years induration. It is important to note that it is not possible for one variable tohave a shorter-term impact on another variable than the original impacton itself. This is coherent with the purpose of the scenario assessments,to illustrate the impact of selected scenarios on the system as a whole,addressing step B3.

4.2.6. Analysing Ecosystem Service Value ChainsA structured, demand-side approach - starting from the disturbance

through to the final impact - was used to conduct the identification andanalysis of ES value chains. From the scenario-based CLDs, relevantlinear causal pathways (value chains) that impact flood attenuationwere identified. Examining individual value chains facilitated theidentification of the ideal areas (i.e. leverage variables) to intervene tobest reduce (increase) the negative (positive) impact of the systemchange on the final ES. Potential leverage points in these value chainscan be single or multiple environmental and/or anthropogenic vari-ables and/or any of the linkages between them. In the case of qualita-tively defined causal relationships, no robust conclusions can be drawnas to the impact on the final ES when there are multiple causal path-ways affecting the same variable in opposite directions. Moreover, thisapproach does not directly incorporate human responses/behaviouralfeedbacks to system disturbances. Nevertheless, informed deductionscan be made about the time scale of the impact and potentially themagnitude of different impacts if the ecosystem processes and dynamicsare well understood.

The identification of individual value chain examples allows forlinear visualisation and evaluation of how complex changes to thesystem affect the provision of final ESs. Using these examples, potentialmanagement options were explored for each of the scenarios to providefuture planning opportunities to improve the positive impacts or miti-gate the negative impacts on the provision of the final services. Thisapproach does not directly incorporate human responses/behaviouralfeedbacks.

The scenario analysis and ES value chain analysis processes areiterative in nature and can be alternated between to provide as much

detail as needed. If the outcome of the analyses still do not providesufficient information to make a decision around the stated objective,the problem or objective can be reconceptualised to account for thecomplexity of the system and/or limitations of the model (Fig. 1). Thecombination of these processes address steps A5, C2 and C3.

5. Results

Fig. 3 illustrates the CLD representing the complex array of inter-mediate aquatic ESs in the Baviaanskloof catchment and how theseaffect the provision of three final ESs in a multi-dimensional snapshot.Each causal linkage (arrow) qualitatively indicates the relationshipbetween the two variables that it connects. Due to the complex andstochastic nature of the system under study and the tiered structure ofthe intermediate ESs, no positive or negative feedback loops wereidentified. Kirkwood (2013) and Sterman (2000) identify this type ofapproach as open loop thinking or ‘pejorative thinking’. This approachis appropriate for systems that do not contain an explicit number ofvariables and/or interactions.

Two scenarios were identified that would impact flood attenuation,fire is seen as a threat to the flood attenuation capacity of the systemwhile catchment restoration can improve flood attenuation capacity.The subsequent ES value chain analysis provides insight into severallinear causal pathways that impact flood attenuation positively or ne-gatively and evaluate where in the value chain management interven-tions would be most effective.

5.1. Fire Scenario

The fire scenario is an example of a short-term event that simulatesa once off fire event in the Baviaanskloof catchment. This simulationreplicates a fire severe enough to significantly reduce the amount ofnatural vegetation in the area without affecting crops or livestock.While simultaneously not being hot enough to have any pertinent im-pacts on the soil properties that support the vegetation and associatedsoil processes. Hence, the only variable that was directly affectedthrough the fire scenario in the CLD was natural vegetation.

Analysing this event as a scenario illustrates the various ripple ef-fects that transpire from this episode over the three aforementionedtime scales, as exemplified in Fig. 4. The CLD demonstrates how thedisturbance variable (i.e. fire) will have an impact on all three of thefinal aquatic ESs. It is clear that the fire would decrease the ability ofthe system to attenuate floods immediately afterwards. However, it isimportant to note that this outcome does not take into considerationany knock-on effects such as the regrowth of vegetation over time,which could counter these negative impacts.

5.2. Catchment Restoration

The catchment restoration scenario is a demonstration of some ofthe restoration activities that are currently being performed in theBaviaanskloof catchment. Considering all of these restoration activitiesbroadly, the scenario simulates the impact of increasing the surfaceroughness within the catchment and the associated impacts throughoutthe system. Fig. 5 illustrates how catchment restoration activities canhave a mixed impact on the provision of the three final aquatic ESs asan indirect result of increasing catchment roughness. The system'sability to attenuate floods will be explicitly increased in the immediateterm through a decrease in flow velocity and an increase in floodplaincapacity.

Fig. 6 provides a key of the different variables and arrows re-presented in the CLDs.

5.3. Flood Attenuation Value Chain Analysis

The scenario analyses illustrated above outline some of the complex

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Fig.

4.Fire

scen

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CLD

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Fig.

5.Catch

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interactions within the system that arise from naturally and anthro-pogenically induced changes. The outcomes of these analyses arecomplex in themselves and not straightforward enough for manage-ment and decision-making purposes.

Fig. 7 demonstrates three examples of linear causal pathways(segments of ES value chains) showing the effect of fire and catchmentrestoration on the system's ability to attenuate floods. The loss of nat-ural vegetation because of a fire event indirectly decreases the flood-plain capacity and increases flow velocity; both of which have a ne-gative impact on flood attenuation (i.e. ↑ fire = ↓ naturalvegetation = ↓ infiltration rate = ↑ surface water flow = ↑ flow velo-city = ↓ flood attenuation). Thus, the apparent leverage variables forintervention would be natural vegetation, roughness and infiltrationrate. Propagating and promoting the growth of fire resistant indigenousplants would theoretically reduce the loss of vegetation resulting fromfire and increase system roughness and the infiltration rate (Booysenand Tainton, 1984). This could be supplemented with a geologicalsurvey that identifies the most efficient and effective areas to promoteinfiltration (e.g. closest to the phreatic or saturated zone). Alternatively,the aforementioned catchment restoration scenario, directly and over arelatively fast time scale, improves the flood attenuation capacity in theBaviaanskloof catchment (i.e. ↑ catchment restoration = ↑ rough-ness = ↓ flow velocity = ↓ erosion = ↑ floodplain capacity = ↑ floodattenuation).

Identifying relevant leverage variables that are required for theprovision of a particular final ES can provide crucial information forprivate and public decision makers. For example, insurance companiesinterested in the provision of flood attenuation could utilise this ap-proach to identify possible intervention measures that will reduce theimpact of fire on the ability of the system to attenuate floods. Fireprovides a potential financial threat to an institution of this nature byreducing the ability of the system to attenuate flooding, as a result moreflood damage claims are put forward. Thus, if a cost-effective option to

Fig. 6. CLD variable and arrow keys.

Fig. 7. Linear causal pathways impacting flood attenuation.

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reduce the effect that fire has on the ability of the system to attenuateflooding can be identified, this could constitute a profitable under-taking.

If these specific relationships between fire and flood attenuationhighlighted in Fig. 7 are quantified, then it would be possible for thefirm to conduct a benefit-cost analysis to determine whether the in-vestment would be financially viable or not. Aside from other apparentbenefits such as improved water retention in the system, which willbenefit local residents etc. This method could also be adopted to de-velop disaster management plans by government with the aim of pre-venting the loss of life and infrastructural damage. The determinationof linear causal pathways facilitates the mapping and qualitative ana-lysis of system variables to identify opportunities and potential leveragepoints for improvement/maintenance of final ES provision. Althoughvalue is not empirically traced from production to consumption, thisillustrates an alternative way of analysing ES value chains.

6. Discussion and Conclusion

In light of existing and predicted consequences of ecosystem de-gradation and the recent increase in scientific ES research, modernsociety is undergoing a seminal change in the way benefits of naturalecosystems are understood and valued (Maltby and Acreman, 2011).There is global consensus among scientists and economists alike thatthe natural systems providing the necessary services that support anddrive modern day society have entered a period of drastic change(Carpenter et al., 2009; De Groot et al., 2010b; Griggs et al., 2013; MA,2005; O'Neill et al., 2010; Ostrom, 2009; Rockström et al., 2009;Wallace, 2008). The concept of ESs is an extension of both ecologicalfunctioning and economic externalities that creates a nexus between thefields of ecology and economics, despite their conflicting ideologies(Fisher et al., 2009; Gómez-Baggethun and Barton, 2012).

The ESVCA framework presented here attempts to narrow the gapbetween traditional, linear economic thought and the complex dynamicsystems they attempt to model. The tool is more inclusive than currentenvironmental management models, as it includes environmental andanthropogenic components that contribute towards the provision offinal ESs in a flexible manner to accommodate system complexity.Model outputs are predictive in nature, allowing pro-active strategies tobe implemented through the identification of potential future systemthreats via relevant scenario analyses. The decision-making tools gen-erated from the ESVCA framework promote systemic thinking withinmanagement endeavours, a need widely acknowledged across dis-ciplines (Sterman, 2000).

The ESVCA framework has the potential to incentivise private andpublic investment into the sustainable management of ecosystems byvisually demonstrating the various system processes that contribute tothe generation of marketed and non-marketed goods and services. Thishas the potential to assist with the integration of ESs into formal mar-kets and simultaneously generate benefits associated with healthyecosystem functioning. Trade-off analysis between various ESs is fa-cilitated through this model as individual inputs and their potential toadd value are compared and scrutinised (Brauman et al., 2007). Themethod can address issues of changing ecological feedbacks, however,currently cannot account for potential regime shifts within ecologicalsystems (Carpenter et al., 2009).

The primary limitation associated with a tool of this nature is itsinability to accurately account for changes in spatial scales because ithas to be calibrated to a specific area. Due to the complex, stochasticand dynamic nature of socio-ecological systems, it is difficult to delimitthe optimal size of a CLD that minimises model uncertainty whilemaintaining sufficient complexity to realistically represent the system(Loucks et al., 2005). Without empirically defining the causal re-lationships between the different variables, it is not possible to de-termine the magnitude of impacts. Hence, when there are two con-flicting impacts on one variable, one cannot categorically deduce the

direction of causality. Similarly, each system relationship had to beclassified exclusively as either positive or negative, thus, the qualitativemethod cannot capture potential socio-ecological thresholds.

This approach cannot capture the impacts of differing magnitudes ofchange if they result in a change in the causality of the relationship. Forexample, a small increase in one variable could have a positive impacton the next variable, but a large increase in that same variable couldresult in a negative impact on the next variable. Furthermore, thescenario analyses were limited to analysing one scenario at a time. Thisis a result of numerous conflicting impacts on the same variable oc-curring when too many influences are included at the same time. Thus,associated knock-on impacts cannot be simultaneously analysed, forexample, knowledge of an impending fire might change human beha-viour in terms of land use, which may affect the magnitude of theoutcome of flooding on the system.

Future research directions lie in the incorporation of the ESVCAapproach into multiple framework modelling and decision-makingprocedures, such as integrated environmental assessments (Toth andHiznyik, 1998). Combining complex models alongside multiple deci-sion frameworks will provide the best opportunity to generate credible,legitimate and salient information (Cash et al., 2003; Mace, 2016).Practical assessment of decision problems involving many decisionmakers and system variables generally use Pareto-optimal solution sets(Lund and Palmer, 1997), the ESVCA framework can contribute to-wards multi-criteria decision analyses to generate optimal solutions.Quantifying the magnitude of the relationships between various inter-mediate and final ESs will facilitate deeper analysis of ES value chainsand the associated system impacts of interventions and disturbances.

The efficacy of the ESVCA framework as a decision-making tool isprimarily limited by data and information availability, which sig-nificantly hampers large scale analysis. Nevertheless, qualitative ana-lyses developed using expert knowledge and public participation aregaining momentum in environmental planning initiatives (Fraser et al.,2006). Aside from data limitations, managing the trade-off betweensystem complexity and practicality of the tool will be largely de-termined by the purpose of its application. This flexibility facilitates thedevelopment of smaller decision-support oriented tools, as well ashighly complex research oriented applications.

The ESVCA framework developed in this paper illustrates a mea-sured and systematic approach towards mapping and analysing thevalue chains of intermediate ESs that contribute towards the productionof final ESs. Through the use of CLDs, this approach enables the iden-tification of forward linkages and ripple effects in linear value chains offinal ESs and the identification and assessment of challenges and op-portunities in these value chains. Ultimately, facilitating the develop-ment of strategies and recommendations to improve the efficiency andeffectiveness of final ES provision. Continued development of theESVCA framework will contribute towards the advancement of a stan-dardised value chain analysis process for ESs to improve the quantityand quality of ES-based information available to policy- and decision-makers alike.

Acknowledgements

The authors wish to thank the participants of the expert workshopson Complex Aquatic Ecosystem Service Interactions at RhodesUniversity and relevant specialists for sharing practical insight andassisting with the development and advancement of the CLDs. Formalthanks and acknowledgements are extended to the South African WaterResearch Commission (Project K5/2341) for funding the research andthe Council for Scientific and Industrial Research (CSIR) for co-ordinating and managing the project. All responsibility for errors andomissions lies with the authors.

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